Sequencing at the syllabic and supra-syllabic levels during speech perception: an fMRI study
Bibliographic record
Abstract
The processing of fluent speech involves complex computational steps that begin with the segmentation of the continuous flow of speech sounds into syllables and words. One question that naturally arises pertains to the type of syllabic information that speech processes act upon. Here, we used functional magnetic resonance imaging to profile regions, using a combination of whole-brain and exploratory anatomical region-of-interest (ROI) approaches, that were sensitive to syllabic information during speech perception by parametrically manipulating syllabic complexity along two dimensions: (1) individual syllable complexity, and (2) sequence complexity (supra-syllabic). We manipulated the complexity of the syllable by using the simplest syllable template-a consonant and vowel (CV)-and inserting an additional consonant to create a complex onset (CCV). The supra-syllabic complexity was manipulated by creating sequences composed of the same syllable repeated six times (e.g., /pa-pa-pa-pa-pa-pa/) and sequences of three different syllables each repeated twice (e.g., /pa-ta-ka-pa-ta-ka/). This parametrical design allowed us to identify brain regions sensitive to (1) syllabic complexity independent of supra-syllabic complexity, (2) supra-syllabic complexity independent of syllabic complexity and, (3) both syllabic and supra-syllabic complexity. High-resolution scans were acquired for 15 healthy adults. An exploratory anatomical ROI analysis of the supratemporal plane (STP) identified bilateral regions within the anterior two-third of the planum temporale, the primary auditory cortices as well as the anterior two-third of the superior temporal gyrus that showed different patterns of sensitivity to syllabic and supra-syllabic information. These findings demonstrate that during passive listening of syllable sequences, sublexical information is processed automatically, and sensitivity to syllabic and supra-syllabic information is localized almost exclusively within the STP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".